Data Visualization

1 Data Visualization with Base R

• Data visualisation important in understanding our data
• Base R have a good graphing capability

1.1 Practical

• generate some random number e.g. normal distribution with rnorm(_) function

```{r}
rand_ds <- data.frame(x = rnorm(1000, mean = 10, sd = 1), 
                      y = rnorm(1000, mean = 5, sd = 2))
rand_ds
```

• plot scatterplot using plot(_) function

```{r}
plot(x = rand_ds$x, y = rand_ds$y)
```

2 Data Visualization with ggplot2

• There are various packages that offer powerful graphing capabilities, most famous is ggplot2:: package
• ggplot2 were initially developed independently, but later harmonised with tidyverse packages
• based on “grammar of graphics” philosophy:
  • specifying datasets,
  • aesthetic mappings,
  • geometric objects,
  • statistical transformations,
  • etc: scales, coordinate systems, and facets

```{r}
library(ggplot2)

ggplot(rand_ds, aes(x, y)) + geom_point()
```

2.1 Practical

In this practical, we will use data asthmads_clean.sav from dataset folder

0. Load related packages
1. Import dataset

• Create new column, to calculate weight difference

```{r}
library(tidyverse)
```

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ lubridate 1.9.3     ✔ tibble    3.2.1
✔ purrr     1.0.2     ✔ tidyr     1.3.1
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

```{r}
library(haven)

asthmads_clean <- read_sav("asthmads_clean.sav") %>% 
  as_factor() %>% 
  mutate(Wt_Diff = Weight_Post - Weight_Pre)
asthmads_clean
```

1. With ggplot2, you begin a plot with the function ggplot(_), defining a plot object that you then add layers to.

```{r}
ggplot(data = asthmads_clean)

asthmads_clean %>% 
  ggplot(data = .)
```

2. Next, we need to tell ggplot(_) how the information from our data will be visually represented.

• The mapping argument defines how variables are mapped to visual properties (aesthetics) of the plot.
• The mapping argument is always defined in the aes(_) function,
• the x and y arguments of aes(_) specify which variables to map to the x and y axes.

```{r}
asthmads_clean %>% 
  ggplot(data = ., 
         mapping = aes(x = PA_HW, 
                       y = Wt_Diff))
```

3. Our “empty canvas” now has more structure - Physical Activity in x-axes and Weight Difference in y-axis
4. We then need to define a geometrical object that a plot use to represent data

• in ggplot2, function start with geom_
• to plot scatter plot, we use geom_point(_) function

```{r}
asthmads_clean %>% 
  ggplot(data = ., 
         mapping = aes(x = PA_HW, 
                       y = Wt_Diff)) + 
  geom_point()
```

5. We can add aesthetics and layers

• for example, we want to layer the plot by gender
• to add layer, we need to modify the aesthetic (rather than geom)

```{r}
asthmads_clean %>% 
  ggplot(data = ., 
         mapping = aes(x = PA_HW, 
                       y = Wt_Diff,
                       colour = Gender)) + 
  geom_point()
```

3 Other Charts

3.1 Bar Chart

• suitable for categorical data

```{r}
asthmads_clean %>% 
  ggplot(aes(Gender)) + 
  geom_bar()
```

• add colour with fill parameter

```{r}
asthmads_clean %>% 
  ggplot(aes(x = Gender, fill = Gender)) + 
  geom_bar()
```

3.2 Histogram

• unlike categorical variables, numerical variables use histogram instead
• commonly used to visualised the data distribution

```{r}
asthmads_clean %>% 
  ggplot(aes(Weight_Pre)) + 
  geom_histogram()
```

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

• we can adjust fill to make clearer

```{r}
asthmads_clean %>% 
  ggplot(aes(x = Weight_Pre)) + 
  geom_histogram(fill = "white", colour = "black")
```

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

• we can also set the bin width size

```{r}
asthmads_clean %>% 
  ggplot(aes(x = Weight_Pre)) + 
  geom_histogram(fill = "white", colour = "black", binwidth = 2)
```

3.3 Line chart (1)

• Line chart usually use to visualised trend
• we use newly form data in this example

```{r}
time_ds <- tibble(time = 1:10, 
                     value = c(2, 3, 5, 7, 8, 9, 10, 12, 14, 15))
time_ds
```

• here, we want to plot the value by time

```{r}
time_ds %>% 
  ggplot(aes(x = time, y = value)) +
  geom_line()
```

3.4 Line chart (2)

• create line chart, visualising the weight changes
• however we need to wrangle, in long format

```{r}
asthmads_clean %>% 
  select(idR, Gender, Weight_Pre, Weight_Post) %>% 
  pivot_longer(cols = starts_with("Weight"), 
               names_to = "event",
               values_to = "weight")
```

• add geom_point(_)
• add `geom_line(_)
  • add group parameter, the line “grouped” by idR

```{r}
asthmads_clean %>% 
  select(idR, Gender, Weight_Pre, Weight_Post) %>% 
  pivot_longer(cols = starts_with("Weight"), 
               names_to = "event",
               values_to = "weight") %>% 
  ggplot(aes(x = event,
             y = weight)) +
  geom_point() + 
  geom_line(aes(group = idR))
```

• we can add gender as layer, adjusting the aes

```{r}
asthmads_clean %>% 
  select(idR, Gender, Weight_Pre, Weight_Post) %>% 
  pivot_longer(cols = starts_with("Weight"), 
               names_to = "event",
               values_to = "weight") %>% 
  ggplot(aes(x = event,
             y = weight, 
             colour = Gender)) +
  geom_point() + 
  geom_line(aes(group = idR))
```

4 Bonus: Malaysia Map

• load the prevalence of Known DM among Malaysian (data source: NHMS 2019 report, page 36)

```{r}
nhms19_adm <- read_csv("../dataset/nhms19_adm.csv")
```

Rows: 16 Columns: 4
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (1): State
dbl (3): Prevalence, Upper_CI, Lower_CI

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

```{r}
nhms19_adm
```

• download geojson file from dosm github

```{r}
#| eval: false

download.file(
  url = "https://raw.githubusercontent.com/dosm-malaysia/data-open/main/datasets/geodata/administrative_1_state.geojson",
  destfile = "administrative_1_state.geojson",
  mode = "wb")
```

• load sf package

```{r}
library(sf)
```

Linking to GEOS 3.11.2, GDAL 3.6.2, PROJ 9.2.0; sf_use_s2() is TRUE

• harmonised (wrangling) the data
  • load the map

```{r}
my_state_sf <- read_sf("administrative_1_state.geojson")
my_state_sf
```

• combine prevalence dataset with map dataset

```{r}
#| eval: false

left_join(nhms19_adm, my_state_sf)
```

• adjustment maybe needed due to differing column name

```{r}
my_state_sf <- my_state_sf %>% 
  rename(State = state)
my_state_sf

full_join(nhms19_adm, my_state_sf)
```

Joining with `by = join_by(State)`

• the data didnt join for WP. further adjustment should be made

```{r}
my_state_sf <- my_state_sf %>% 
  mutate(State = fct_recode(State,
                            "WP Kuala Lumpur" = "W.P. Kuala Lumpur", 
                            "WP Putrajaya" = "W.P. Putrajaya", 
                            "WP Labuan" = "W.P. Labuan"))

nhms19_adm_m <- full_join(nhms19_adm, my_state_sf)
```

Joining with `by = join_by(State)`

```{r}
nhms19_adm_m
```

• Plotting with geom_sf(_) function

```{r}
#| eval: false

nhms19_adm_m %>% 
  ggplot(aes(fill = Prevalence)) + 
  geom_sf()
```

• seems our dataframe is not sf object.

```{r}
nhms19_adm_sf <- st_as_sf(nhms19_adm_m)
nhms19_adm_sf
```

• Plotting with geom_sf(_) function

```{r}
nhms19_adm_sf %>% 
  ggplot(aes(fill = Prevalence)) + 
  geom_sf()
```

• we can also change colour to look like heat map

```{r}
nhms19_adm_sf %>% 
  ggplot(aes(fill = Prevalence)) + 
  geom_sf() + 
  scale_fill_gradient(low = "green", high = "red")
```

• we can also remove the element

```{r}
nhms19_adm_sf %>% 
  ggplot(aes(fill = Prevalence)) + 
  geom_sf() + 
  scale_fill_gradient(low = "green", high = "red") +
  theme_bw() +
  theme(axis.text = element_blank(),
        axis.title = element_blank(),
        axis.ticks = element_blank(),
        panel.grid = element_blank(),
        panel.border = element_blank())
```